AI Article Synopsis
- The study develops a detailed numerical model for a micro-planar proton-conducting solid oxide fuel cell that focuses on anode-supported fuel cells with methane reforming.
- The model incorporates several complex equations related to mass transfer, chemical reactions, and energy to predict how the fuel cell operates.
- Results indicate that adjusting the air-to-fuel ratio affects current and power density, with a specific improvement noted at an A/F ratio of 0.5; the artificial neural network used for predictions displays high accuracy.
Article Abstract
The current study presents a multiphysics numerical model for a micro-planar proton-conducting solid oxide fuel cell (H-SOFC). The numerical model considered an anode-supported H-SOFC with direct internal reforming (DIR) of methane. The model solves coupled nonlinear equations, including continuity, momentum, mass transfer, chemical and electrochemical reactions, and energy equations. Furthermore, The numerical model results are used in artificial intelligence (AI) models, the K-nearest neighbour (KNN) and, artificial neural network (ANN), to predict the current density and power density of the H-SOFC. The results show that increasing the air-to-fuel (A/F) ratio decreases the current density and overall cell power. In particular, improvements in power and current density observed in H-SOFC when the A/F ratio is set to 0.5, resulting in a respective increase of 2 % and 7 % compared to the initial state at A/F = 1. With an error rate of less than 1 % and an R-score of around 99 %, the ANN model shows good agreement with the numerical results.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696671 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e40996 | DOI Listing |
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